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利用形态计量分析对自动局灶性皮质发育不良检测进行外部验证。

External validation of automated focal cortical dysplasia detection using morphometric analysis.

机构信息

Department of Epileptology, University Hospital Bonn, Bonn, Germany.

Swiss Epilepsy Clinic, Klinik Lengg AG, Zurich, Switzerland.

出版信息

Epilepsia. 2021 Apr;62(4):1005-1021. doi: 10.1111/epi.16853. Epub 2021 Feb 27.

Abstract

OBJECTIVE

Focal cortical dysplasias (FCDs) are a common cause of drug-resistant focal epilepsy but frequently remain undetected by conventional magnetic resonance imaging (MRI) assessment. The visual detection can be facilitated by morphometric analysis of T1-weighted images, for example, using the Morphometric Analysis Program (v2018; MAP18), which was introduced in 2005, independently validated for its clinical benefits, and successfully integrated in standard presurgical workflows of numerous epilepsy centers worldwide. Here we aimed to develop an artificial neural network (ANN) classifier for robust automated detection of FCDs based on these morphometric maps and probe its generalization performance in a large, independent data set.

METHODS

In this retrospective study, we created a feed-forward ANN for FCD detection based on the morphometric output maps of MAP18. The ANN was trained and cross-validated on 113 patients (62 female, mean age ± SD =29.5 ± 13.6 years) with manually segmented FCDs and 362 healthy controls (161 female, mean age ± SD =30.2 ± 9.6 years) acquired on 13 different scanners. In addition, we validated the performance of the trained ANN on an independent, unseen data set of 60 FCD patients (28 female, mean age ± SD =30 ± 15.26 years) and 70 healthy controls (42 females, mean age ± SD = 40.0 ± 12.54 years).

RESULTS

In the cross-validation, the ANN achieved a sensitivity of 87.4% at a specificity of 85.4% on the training data set. On the independent validation data set, our method still reached a sensitivity of 81.0% at a comparably high specificity of 84.3%.

SIGNIFICANCE

Our method shows a robust automated detection of FCDs and performance generalizability, largely independent of scanning site or MR-sequence parameters. Taken together with the minimal input requirements of a standard T1 image, our approach constitutes a clinically viable and useful tool in the presurgical diagnostic routine for drug-resistant focal epilepsy.

摘要

目的

局灶性皮质发育不良(FCDs)是耐药性局灶性癫痫的常见病因,但常规磁共振成像(MRI)评估往往无法检测到。通过对 T1 加权图像进行形态计量分析,例如使用 2005 年引入的 Morphometric Analysis Program(MAP18),可以促进视觉检测,该方法已独立验证其具有临床益处,并成功集成到全球众多癫痫中心的标准术前工作流程中。在这里,我们旨在开发一种基于这些形态计量图的稳健自动 FCD 检测人工神经网络(ANN)分类器,并在大型独立数据集上探测其泛化性能。

方法

在这项回顾性研究中,我们基于 MAP18 的形态计量输出图创建了用于 FCD 检测的前馈 ANN。ANN 在 113 名经手动分割的 FCD 患者(62 名女性,平均年龄±标准差=29.5±13.6 岁)和 362 名健康对照者(161 名女性,平均年龄±标准差=30.2±9.6 岁)的图像上进行训练和交叉验证,这些图像分别来自 13 台不同的扫描仪。此外,我们还在一个独立的、未见过的 60 名 FCD 患者(28 名女性,平均年龄±标准差=30±15.26 岁)和 70 名健康对照者(42 名女性,平均年龄±标准差=40.0±12.54 岁)的独立数据集中验证了训练好的 ANN 的性能。

结果

在交叉验证中,ANN 在训练数据集上的特异性为 85.4%时,敏感性为 87.4%。在独立验证数据集上,我们的方法仍然达到了 81.0%的敏感性,同时保持了 84.3%的高特异性。

意义

我们的方法可以稳健地自动检测 FCDs,并且具有良好的泛化性能,在很大程度上独立于扫描部位或 MR 序列参数。结合标准 T1 图像的最小输入要求,我们的方法构成了药物难治性局灶性癫痫术前诊断常规中一种具有临床可行性和实用性的工具。

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